Photodegradation by sunlight can be an important pathway for the decay of manmade pollutants and microorganisms in the natural environment. This rate of photodegradation depends on the solar irradiance. While reference spectra are available to estimate photodegradation rates on “clear sky” days, cloud coverage is expected to have a large impact on irradiance and consequently photodegradation rates. This research aimed to quantify and predict the impact of cloud coverage on UVB, UVA, and PAR irradiance. Irradiance and cloud coverage data from 2017-2020 was acquired from the National Renewable Energy Laboratory located in Golden, CO and was processed and transformed to serve as inputs for a multivariate and two machine learning models (random forest and shallow neural networks). Because of the nonlinear dependence of irradiance on the time of year and time of day, and an unknown relationship with cloud coverage, multivariate regression did not produce useful results without transformations of the input data. With data transformation, multivariate regression performed well. Nevertheless, random forest regression provided better results with root mean squared errors of 0.16, 5.6, and 65 W/m<sup>2</sup> for UVB, UVA, and PAR irradiance, respectfully, which was an improvement of 17-20%. We also found that irradiance decreased nonlinearly as a function of cloud coverage, and decreases up to 60% can occur depending on the time of year. These results show that cloud coverage can have a substantial effect on environmental photodegradation rates and provide a quantitative estimate of this potential impact.

Quantifying the impact of cloud coverage on irradiance

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.